Characterizing Mobility and Network Usage in a Corporate Wireless
Local-Area Network
Magdalena Balazinska
MIT Laboratory for Computer Science
Paul Castro
IBM T.J. Watson Research Center
mbalazin@lcs.mit.edu
pcastro@us.ibm.com
Abstract
Wireless local-area networks are becoming increasingly popular. They are commonplace on university campuses and inside corporations, and they have started to
appear in public areas [17]. It is thus becoming increasingly important to understand user mobility patterns
and network usage characteristics on wireless networks.
Such an understanding would guide the design of applications geared toward mobile environments (e.g., pervasive
computing applications), would help improve simulation
tools by providing a more representative workload and
better user mobility models, and could result in a more
effective deployment of wireless network components.
Several studies have recently been performed on wireless university campus networks and public networks. In
this paper, we complement previous research by presenting results from a four week trace collected in a large corporate environment. We study user mobility patterns and
introduce new metrics to model user mobility. We also
analyze user and load distribution across access points.
We compare our results with those from previous studies
to extract and explain several network usage and mobility
characteristics.
We find that average user transfer-rates follow a power
law. Load is unevenly distributed across access points
and is influenced more by which users are present than
by the number of users. We model user mobility with
persistence and prevalence. Persistence reflects session
durations whereas prevalence reflects the frequency with
which users visit various locations. We find that the probability distributions of both measures follow power laws.
1
Introduction
Several recent studies characterize the usage of various wireless networks [3, 8, 9, 10, 15, 16]. Tang and
Baker [15] focused on a university building and traced the
activity of 74 users over 12 weeks. Kotz and Essien [8, 9]
studied a university campus network with 1706 users
scattered through 161 buildings with a total of 476 ac-
cess points. Balachandran et al. [3] examined usage of a
wireless network in a large auditorium during a three day
conference. Tang and Baker [16] also studied the Metricom metropolitan-area packet radio wireless network, a
public network with approximately 25,000 radios. Lai et
al. [10] analyzed a combined wireless and wired network,
but that study was limited to only eight users.
Each study presents patterns of user mobility and network usage characteristics for one particular domain. In
this paper, we complement these studies by presenting results from a four week trace gathered on a corporate wireless local-area network (WLAN). Our trace presents the
activity of 1366 users. We use our trace as well as results
from previous research to extract common characteristics
of WLAN usage and to highlight and explain usage differences. We focus on population characteristics, load
distribution across access points (APs), user level of activity, and user mobility.
We find that variations in the number of wireless users
over time closely follow patterns of the underlying population, even though most users access the wireless network a fraction of days and a fraction of time. Hence,
the number of users on a network might be adequately
modeled by scaling down general population models.
Our study shows that there exist large personal differences in users’ mobility as well as in their data transfer
rates. Some users transfer over 1Mbps on average while
others transfer less than 10Kbps on average. In general,
we find that user average transfer rates follow a power
law. The aggregate data transfer rate seen by an access
point does not seem to depend on the number of users associated with the access point, but rather on which users
are present. In each building, approximately 30% of access points owe over 40% of their load to the most active
10% of users on the network. Location of an access point
also plays a role in the aggregate load it observes.
Users spend a large fraction of their time and long
periods of time at a single location, which we call their
home location. Interestingly, they do not reduce their network usage when moving away from that location and
changing location more frequently. We model user mobility with persistence and prevalence. Persistence mea-
sures how long users stay continuously associated with
the same access point and prevalence reflects how frequently users visit various locations. Our definitions are
based on Paxson’s definitions of “routing persistence”
and “routing prevalence” in his study of Internet routing stability [13]. We find that the probability distributions of both measures follow power laws. We use prevalence metrics to classify users into different mobility categories. We find that 50% to 80% of users are occasionally or somewhat mobile: they spend most of their time
at a single location, but periodically visit other locations.
Comparing our results with other studies, we find
many similarities in mobility and network usage characteristics. We find that these characteristics are best explained by factors orthogonal to whether the network runs
on a campus, in a corporation, or in a public environment.
The main factors influencing network usage include personal differences between users and function of various
locations (including scheduled events). The main differences in user mobility appear among locations serving as
primary places of work and locations visited occasionally. The density of resources (classrooms, conference
rooms), and differences between individual users also influence mobility significantly.
The rest of the paper is organized as follows. We first
present the methodology used to gather our trace in Section 2. In Section 3, we describe the characteristics of
our user population and contrast it with the population in
previous studies. In Section 4, we describe load distribution across access points and analyze factors influencing
access point load. Section 5 presents and compares user
mobility characteristics in each environment. We also introduce metrics for describing these characteristics. In
Section 6, we discuss how some of our findings may benefit network deployment, application design, and simulation of user mobility. We conclude in Section 7.
2
work through access points distributed in the environment. All 177 access points are Cisco Aironet 350s. We
observed a total of 1366 unique MAC addresses. Laptops
were by far the predominant devices on the network. We
do not have information whether any other types of devices were used at all. We assume that each unique MAC
address corresponds to a user, even though it is possible
for a single user to have more than one MAC address or
for users to trade cards with each other.
We used SNMP [4] to poll access points every 5 minutes, from Saturday, July 20th 2002 through Sunday, August 17th 2002. We chose 5 min intervals to ensure that
our study would not affect access point performance. We
collected information about the traffic going through each
access point as well as about the list of users associated
with each access point. For each user, we retrieved detailed information on the amount of data (bytes and packets) transferred, the error rates, the latest signal strength,
and the latest signal quality. We polled all access points
except three located in MBldg that did not respond to
SNMP requests.
Due to a power failure, there is a one-hour hole in the
data (07/30/2002 from 1pm to 2pm). For unknown reasons, we also have a few holes in the data gathered at
a few of the access points during the evening and night
of 08/08/2002. Due to periods where access points were
heavily loaded, some sample intervals stretch to 10 min.
Users were not informed that the study was performed.
The only sensitive information that we gathered were the
MAC and IP addresses of network cards, as well as the
names assigned to access points. To ensure user privacy,
we anonymized all three types of information. We did not
map access points to explicit locations or track individual
users. We only present aggregate results.
All data from our trace is available for download
at the following location: http://nms.lcs.mit.
edu/˜mbalazin/wireless/.
Methodology
3
The 802.11b wireless local-area network that we studied is spread throughout three large corporate buildings
hosting computer science and electrical engineering research groups. The largest of the buildings, which we
call LBldg, has 131 access points and is approximately
10 miles away from the other buildings. The other buildings, MBldg and SBldg, are adjacent to each other. They
have 36 and 10 access points respectively. The placement of access points in buildings is based on geometry
(one access points per corridor, for instance). Extra access points are placed in a few highly used rooms, such
as a customer laboratory in SBldg.
The network is configured to run in infrastructure
mode, in which wireless clients connect to the wired net-
Wireless user population characteristics
We saw a total of 1366 distinct users in our four week
trace: 796 users spent most of their time in LBldg building, 437 in MBldg, and 133 in SBldg. Figure 1 shows the
total number of users present on the network every day of
the trace. Figure 2 shows the number of users present on
the network during different hours of a day on weekdays.
We show the 10th, 50th, and 90th percentile values registered for each hour throughout the trace. For all three
buildings, the patterns reflect the office environment and
normal office work hours. We also note a slight reduction in the number of users around lunch time (Figure 2).
However, since the reduction is small, we conclude that
most users work through lunch or leave their laptops on
Total number of users
Cumulative fraction of users
LBldg
MBldg
SBldg
500
400
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0
07/20
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Date
Figure 1: Total number of wireless users in each building on each day
of the trace. The trace starts on Saturday, July 20th 2002. The figure
show patterns of a normal office work week.
350
Number of users
300
LBldg
MBldg
SBldg
5
10
15
20
25
30
Number of active days
Figure 3:
Number of days users are present in the trace. The distribution is uneven at the edges. A large fraction of users (around 22% to
38%) appear only one or two days in the trace. Less than 10% of users
come more often than the 20 work days of the trace and a fraction of
these are laptops left in offices.
LBldg
MBldg
SBldg
250
200
150
100
50
0
00 02 04 06 08 10 12 14 16 18 20 22 00
Hour
Figure 2: Number of users per hour on weekdays. For each building,
the 10th, 50th, and 90th percentile values for each hour are shown. The
figure shows a strong pattern of regular office work day.
while they eat, so the machine is “present” even if there
is no activity. Also, some users stay late at night or leave
their laptops on when they go home, since the number of
wireless users is greater than zero during the night.
These patterns are similar to those found at university
campus locations used for working (offices, libraries, academic buildings) [8, 9, 15]. They differ from on-campus
locations such as dormitories [8, 9] and public metropolitan networks [16], which users access both from work
and from home. In these environments, peaks in the number of users appear during evening hours. There are also
much lower reductions in the number of users on weekends. Small scale networks such as a single conference
room [3] show much more variability in the number of
users due to the impact of scheduled activities.
Therefore, daily and hourly patterns in numbers of
wireless users on a network are closely tied to patterns
in the underlying population. Differences appear not so
much among public, academic, or corporate networks but
among networks that cover usage at the work place, at
home, or during a specific event.
To further characterize the user population, Figure 3
presents the cumulative distribution of the number of
days each user appeared in the trace. Each user was
counted only in the distribution of the building where the
user spent most of his or her time. The number of days
that users are present varies greatly: only 12% to 25% of
users are present more than 18 out of the 20 work days,
whereas 22% to 38% of users appear only during one or
two days. We suspect that the latter group are outside visitors mostly from other sites that the company has in the
same metropolitan area. It is interesting to note that the
great variety in the number of days that users are present
does not influence the regular pattern shown in Figure 1.
The presence of visitors and the absence of employees
must therefore be uniformly distributed.
In terms of the fraction of days that users access the
network, our distribution is similar to a single building on
a university campus [15]. Compared with a whole campus [8, 9] our trace has more users appearing only one
or two days (visitors) and fewer users appearing more
than 2/3 of the days. The higher uniformity of a campus wide distribution might be related to the fact that the
study tracks many users for prolonged periods of time
(i.e., students living on campus) and not only when they
come to work in specific buildings.
Additionally, we computed the fraction of time users
remain on the wireless network on days where they actually use it. We found that 50% of users remain connected
60% to 100% of the work day.
4
Load distribution across access points
In this section we examine load distribution across access points. According to current guidelines [1, 5], access
LBldg
MBldg
SBldg
0
0.1
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Fraction of users
Cumulative fraction of access points
LBldg
MBldg
SBldg
0
5
10
15
20
1
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LBldg
MBldg
SBldg
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Fraction idle time
Figure 4: Fraction of users seen at each access point throughout the
whole trace. The fraction is computed with respect to users who visited
each building. The figure shows a wide disparity across access points
and across buildings.
1
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Number of users
Figure 5:
Maximum number of users ever simultaneously associated
with each access point. The figure shows a wide disparity across access
points.
points should be distributed based on the physical aspects
of buildings, the signal strength, and signal-to-noise ratios, as well as the number of users and their application
mix. In this section, we examine how load is balanced
across access points in real settings. We examine the user
distribution, the total amount of data transferred, as well
as the data transfer rates. We also examine factors influencing access point load.
4.1 Users
Figure 4 shows the fraction of users seen at each access point throughout the trace. A few access points see a
small fraction of users: 10% of access points in LBldg see
only 2.5% of all users who visited the building. Others
see a greater fraction of users, some as much as 50%. Differences between buildings are partly explained by building sizes (LBldg is much larger than the other two) and
numbers of access points: LBldg has 131 access points,
MBldg has 36, and SBldg has only 10. Since approxi-
Figure 6: Fraction of time that access points are idle during a normal
work week (Monday-Friday, 9am to 6pm). Most access points are used
almost all the time. However, a few access points are idle large fractions
of time. The fraction of time was computed as the fraction of samples
without any users.
mately twice as many users visited LBldg as did MBldg,
LBldg has a much smaller ratio of users to access points
(around 7 versus 16 and 30 for MBldg and SBldg, respectively).
Figure 5 shows the maximum number of users simultaneously associated with each access point. Some access points see few simultaneous users: 40% of access
points never see more than 10 users, while other access
points see as many as 30 simultaneous users. Some of
the access points with the highest numbers of simultaneously associated users correspond to large auditoriums
and cafeterias.
University campuses [8, 9, 15] and large-scale public
networks [16] also see great disparity in the average and
maximum number of users handled by access points.
This is not surprising as these values depend on the popularity of certain locations (auditoriums or cafeterias for
example). Hence, except for small-scale networks [3],
popularity differences appear in all environment studied.
On a small scale, Balachandran et al. [3] find that users
are distributed rather evenly across access points.
Given the regular work schedule of our corporate population, access points are idle (no user is associated with
them) a large fraction of the time when weekends and
nights are considered. However, access points are also
idle some fraction of the time during normal working
hours. Figure 6 shows the fraction of idle time for cumulative fractions of access points in each of the three
buildings. Most access points are used almost all the time
during a work week. For both MBldg and SBldg, 75% of
access points are idle less than 10% of the time. However, 10% of the access points in LBldg are still idle over
75% of the time. On the university campus that Kotz and
Essien study [8, 9], over one third of access points are
0.16
Avg throughput (Mb/s)
Cumulative fraction of access points
1
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20
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Total bytes transferred (GB)
Figure 7: CDF of the amount of data transferred from each access
point to wireless users over the duration of the trace. The figure shows
an uneven usage of resources. Data from August 7th was ignored in
this computation as all access-point counters were reset by a third party
around 19:40 that day.
20
40
60
80
Access point
100
120
(a) Average access point throughput
0.04
LBldg
idle on a typical day. Hence, in all environments, good
coverage requires deploying resources, even in locations
where they are seldom used.
4.2 Data transferred
Std dev of the avg
0.035
0.03
0.025
0.02
0.015
0.01
0.005
Figure 7 shows the total amount of data forwarded
through each access point during the trace. Access points
are ordered by increasing amount of data they forwarded,
and the cumulative fraction of access points is indicated
for each amount. Due to space limitations, we only
present results for traffic going from access points to
wireless users. The graph for the opposite direction is
similar, though with slightly lower values. The amount of
data forwarded varies considerably across access points
(from close to 0 up to 21GB1 ), indicating an uneven usage of resources.
Figure 8 shows the average throughput of each access
point. We define access point throughput or load as the
total amount of bytes an access point forwards for any
associated user in either direction over a given period of
time. We computed these averages in two steps. For each
access point, we first computed the average throughput
for each sampling interval (interval between two consecutive polls of the same access point). We then computed
the mean of these values. Figure 8 shows that much more
data is transferred on average at some access points than
others (with small errors on these averages, as shown in
Figure 8(b)). We obtain similar graphs for the other two
buildings. In the following sections, we discuss these
throughput differences and examine factors that influence
access point load.
1 Throughout
2−3 M Bps
the text, 1M B
= 220 Bytes and 1M bps =
0
0
20
40
60
80
100
120
Access point
(b) Standard error on average
Figure 8: Average throughput for access points in LBldg. Only intervals with users were considered in averages. Access points are ordered
by decreasing average.
4.2.1
Correlation between number of users and load
Figure 9 shows access point throughput for various numbers of associated users for MBldg. Throughputs were
computed over individual polling intervals. The figure
shows that little correlation exists between the number
of users and access point throughput. For more than 14
users, the outliers (99th percentile) seem to decrease, but
this is due to the smaller number of samples with so many
users. We show the results for MBldg as access points
saw highest numbers of simultaneously associated users
most frequently. The graphs for the other buildings show
even less correlation.
We computed the correlation coefficients for each
building, and found 0.10, 0.20, and 0.15 for LBldg,
MBldg, and SBldg respectively. For intervals where access point load exceeded 100Kbps, the number of users
and the load are even less correlated (−0.14, 0.03, and
−0.06). This phenomenon can be explained by noticing
7
99th Percentile
75th Percentile
50th Percentile
6
Throughput (Mb/s)
Throughput (Mb/s)
7
99th Percentile
75th Percentile
50th Percentile
6
5
4
3
2
1
5
4
3
2
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0
0
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8
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Number of users
5
10
15
20
Time of Day
Figure 9: Throughputs measured at access points in MBldg for various numbers of associated users. 50th, 75th, and 99th percentile shown
(only samples over 100Kbps were taken into account). The figure shows
that little correlation exists between the two numbers.
Figure 10: Throughputs (per polling interval) measured at every access points on each hour of the day on every workday in LBldg: 50th,
75th, and 99th percentile (of samples with values over 100Kbps) shown.
No correlation seems to appear between throughput and hour of the day.
that most users are passive most of the time. When only
passive users are present, increasing their number slightly
increases the load. However, as soon as some users become active (i.e., start transferring large amounts of data),
they drastically increase the average throughput and the
influence of other users becomes insignificant.
We located a few access points that had the higher
average transfer rates. They were our “dining conference room,” laboratories, and conference rooms serving
small meetings and teleconferences. These locations differ from most popular and crowded locations corresponding to cafeterias and auditoriums.
Tang and Baker [15] find that: “Usually, the throughput as a whole increases as the number of users increases”
(i.e., the total throughput through routers increases with
the number of users). However, they find that: “the maximum throughput is achieved by a single user and application.” Kotz and Essien [8, 9] also notice little correlation between the number of users and the amount of
traffic going through access points. They find the largest
numbers of users at access points located in lecture halls,
while most traffic comes from residences. However, it
is not clear from their results whether the difference is
attributable to the extra amount of time that users spend
daily on the network at residences or their level of activity
at these locations. Balachandran et al. [3] point towards
the fact that: “load distribution [...] does not directly correlate to the number of users at an access point.” Indeed,
they find that peak load is not achieved when the maximum number of users are present. They also find that although the number of users is almost constant, load varies
considerably over time. We confirm their conclusion on
a larger scale. Additionally, Figure 9 shows that the number of users and the load are rather uncorrelated regardless of whether the load is high or not.
We conclude that offered load and number of users
associated with an access points are weakly dependent
in our environment, but also in the other environments
studied.
4.2.2
Correlation between time of day and load
Figure 10 shows access point throughputs registered on
various hours of the day. Throughputs were computed
separately for each access point and each polling interval. The value was then associated with the hour of the
first poll. The figure shows that little correlation exists between time and throughputs other than the fact that sometimes users are not present on the network, as shown earlier on Figure 2. The correlations coefficients between
time of day and load are 0.016, 0.020, and 0.030 for
LBldg, MBldg, and SBldg respectively. Similarly, in the
trace presented by Balachandran et al. [3], the offered
load oscillated between 0Mbps and 2Mbps as long as
users were present on the network (during morning and
afternoon sessions), showing little correlation between
load and time of day.
Interestingly, in MBldg, we found that the few users
who stay later at night generate only little activity
whereas in LBldg, a lot of activity persists on the network until midnight (as shown on Figure 10). This shows
a difference in the characteristics of the wireless population that did not appear when examining only the number
of users on the network (Figure 2). Also, for all buildings,
users detected around 3am to 6am were idle laptops left
on in offices, since no activity was detected during these
periods.
Even though load on access points is not directly
correlated with the time of day, it may be influenced
by specific events such as regularly scheduled meetings.
For instance, for one of the access points, most peaks
(above 2Mbps) occurred regularly between 12pm and
LBldg
1.89/(x^0.85)
1
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Fraction throughput from active users
Avg transfer rate (Mb/s)
10
User
1
LBldg
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Access point
Figure 11:
Distribution of average individual user transfer rates in
LBldg. Averages follow a power law, except for passive users with
average transfer rates under 10Kbps.
Figure 12: In LBldg, fraction of total throughput attributable to users
with average transfer rates higher than 0.04Mbps. Access point indices
follow those in Figure 8 (decreasing average throughput).
2pm. However, only few access points showed such clear
patterns.
Tang and Baker [15] find some differences among
what they call user sub-communities (users whose offices
are grouped around different access points). They find
that, in general: “While the wings with most users (2b
and 3b) also have the highest peak throughput, the users
on the 3b wing attain that throughput more often.” Other
than this general finding, few conclusions are drawn in
the other studies about personal user differences. We
find that these differences are significant. Since our trace
monitored 1366 users as they worked in offices, attended
meetings, and relaxed in common areas, we believe that
personal differences will extend to other environments as
well.
4.2.3
Personal user differences
Personal user differences are another factor influencing
access point load: Some users are more active than others. To appreciate these differences, we compare the average rates at which users transfer data (in either direction).
For any two consecutive polls, we compute the average
transfer rate of each user who remained associated with
the same access point over the interval. We then computed the average of all values for each user to get the
overall average for that user.
We compare individual average traffic rates to determine personal differences in user’s level of activity.
Figure 11 shows a great disparity among users. Some
users have quite high average traffic rates while others are
hardly ever active. Except for passive users (transfer rate
under 10Kbps), the average user transfer rates follow a
power law distribution. For both MBldg and SBldg, we
find a similarly shaped distribution with a 10Kbps threshold under which the distribution does not follow a power
law anymore.
Whenever users with high network usage characteristics appear, an access point may expect its load to increase significantly. Figure 12 shows the fraction of the
throughput due to users whose average data transfer rates
are above 0.04Mbps. Access point indices correspond to
those of Figure 8 (i.e., decreasing average access point
throughput). These users represent only 10% of all users
however they account for over 40% of the bandwidth usage at over 30% of access points. We obtain similar
graphs for the other buildings. We also looked at users
whose average rate was above 0.02Mbps. They represented 20% of all users, but they were responsible for
over 40% of load at 60% of all access points.
4.2.4
Influence of access point location on load
Figure 12 shows that access points with high average
throughputs (those at lower indices in the figure) owe
slightly larger fractions of their throughputs to the most
active users. Active users also use a large fraction of
bandwidth at locations with little total activity. Therefore,
the fact that some access points have much higher average transfer rates than others is also due to other factors
than which users are present. In this section, we analyze
the impact of access point location on its load by testing
whether location influences users’ level of activity.
To determine whether user’s level of activity depends
on location or not, we perform a one-way classification
analysis of variance [6]. The factor that we are investigating is location. The hypothesis is that there is no effect of location on a user’s data transfer rate. For each
user, this analysis method compares the distribution of
the rates achieved at each location visited (F-test [6]).
We only examine users who visited at least two locations and whose average transfer rate was above 10Kbps.
For LBldg the classification is statistically significant for
27% of users. We reject the hypothesis of location neu-
4.2.5
Access point peak throughput periods
In our data set, we observed many polling intervals where
average access point throughputs exceeded 3Mbps. We
observed extremely few intervals with 5Mbps or higher
averages. Therefore, in this section, we present results
for intervals averaging over 4Mbps. We call such intervals peak throughput periods or peaks. We find that peaks
last short periods of time and seem highly correlated with
location. However, the network we studied was well provisioned and did not seem to experience much overload.
Figure 13 shows the number of polling intervals (consecutive or not) where the average transfer rate exceeded
4Mbps for each of the 131 access points in LBldg. In the
figure, access points are ordered by decreasing number
of peaks. Some access points experience peak throughput periods quite often while the transfer rate at others
never exceeds 4Mbps. A few of these peaks lasted over
an hour, but 48% of them lasted only for one polling
interval in both LBldg and MBldg. The fraction was
64% in SBldg. In [15], Tang and Baker found that in
their network, throughputs greater than 3Mbps were due
mostly to a single user rather than distributed across several users. They also found that some locations were seeing significantly more peaks than others.
We also looked at how often more than one access
point experienced a period of high load. For LBldg,
Number of high transfer rate intervals
trality in all these cases, and conclude that, for these
users, location significantly affects transfer rate. Similarly, we obtain a significant result for 32% of users in
MBldg and 23% of users in SBldg. We reject the hypothesis for all of them. Hence we conclude that user transfer
rates are affected by the location.
From anecdotal evidence, we know that, in our corporation, during talks held in large auditoriums, users
mostly check their email or browse the Web. During conference calls held in small rooms, users go over presentations and download attachments pertaining to the meeting, hence using much more bandwidth. We plan to investigate the relationships between applications and location in future work.
Other studies also seem to find some relationship between location and activity level, mostly because location determines the type of activities that users pursue. Balachandran et al. [3] never see peaks greater than
0.57Mbps for any user. Kotz and Essien [8, 9] find that
the daily throughput per MAC address varies greatly between buildings, with residences seeing much more traffic than social locations. They do not indicate whether
the differences are attributable to higher throughputs or
to the length of time that users spend in each location.
However, they do find differences in the types of applications predominantly used in each campus building.
40
LBldg
35
30
25
20
15
10
5
0
0
20
40
60
80
100
120
Access point
Figure 13:
Distribution of the number of high data transfer rate intervals across access points. Some access points see high transfer rates
quite often while most access points never see high transfer rates (access
points are ordered by decreasing number of peaks).
there were 188 events where load exceeded 3.5Mbps at
some access point. 50 of these events happened at the
same time or within a few minutes of one or two other
events. However, since we do not track access point
location, some of these simultaneous events are probably
unrelated. Hence, most peaks affect a single access point
at the time.
We conclude that individual user differences have a
high impact on access point load: 20% of users account
for 40% of the data transferred at over 60% of the access points. Access point location also influences load,
but much less. Popular locations see many simultaneous
users and a larger fraction of all users, but the number
of users does not influence load significantly. Unpopular locations remain idle most of the time. Time of day
influences the number of users present on the network,
but it does not influence access point load significantly,
even during the night. Finally, many network usage characteristics such as the uneven distribution of users and
load across access points, and the differences in location
popularity, are independent of whether the network is deployed on a university campus, a corporation or at a conference.
5
User mobility characteristics
In this section, we examine user mobility characteristics and compare our results with the characteristics
found in the other studies. We assigned a home building to each user corresponding to the building where they
spent most of their time.
For each building, Table 1 shows the fraction of users
who visited only that building, either of the other buildings, or all buildings. Most wireless users stay within one
LBldg
81%
16%
3%
MBldg
55%
34%
11%
SBldg
72%
26%
2%
25
Number of access points
one bldg
two bldgs
three bldgs
Table 1:
Cumulative fraction of users
Fraction of users who visited one, two or all three buildings.
Each user is counted with the building where the user spent most of his
or her time.
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
LBldg
MBldg
SBldg
20
15
10
5
0
07/20
07/27
08/03
08/10
08/17
Date
Figure 15: Distribution of number of access points visited by users
each day of the trace. The 50th, 75th, and 99th percentiles are represented. Note that graphs for MBldg and SBldg are slightly offset along
the x-axis to improve readability.
LBldg
MBldg
SBldg
0
5
10 15 20 25 30 35 40 45 50
Figure 14: Number of access points visited by users during the whole
trace.
building, but a significant fraction (20% to 45%) move
between two or more buildings. This is mostly the case
for MBldg and SBldg, located near each other. Only a
small fraction (up to 11%) of users visit all three buildings. These mobility patterns are much more constrained
than those found on a university campus [8, 9] where the
median user in their trace visited five buildings. The difference is due to a higher concentration of resources (libraries, conference rooms) within each of our corporate
buildings. Also, many university users both work and live
on campus and visit different campus locations for each
type of activity.
Figure 14 shows the number of access points each user
visited during the trace. The figure shows cumulative
fractions of users who visited increasing numbers of access point. We find that 50% of users in SBldg, MBldg,
and LBldg visited respectively 3, 9, and 7 access points
or fewer. These numbers include users who use their
wireless cards only a few days in the trace. On the other
hand, 50% of users visit between 7 and 40 access points,
with one user visiting as many as 50. University campus
users show even greater mobility disparities than corporate users: the median corporate user visits a similar number of access points as the median university user [8, 9],
but the tails of our distributions (Figure 14) are shorter.
Hence, there might exist small differences between user
populations, but the differences might also be due to the
fact that Kotz and Essien [8, 9] study many users while
attending activities other than working.
Figure 15 shows the distribution of the number of
Cumulative fraction of users
Number of access points
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
LBldg
MBldg
SBldg
0
0.2
0.4
0.6
0.8
Fraction of time
1
Figure 16: Fraction of time that users spend at their home locations.
The fraction of time was measured as the fraction of samples where the
user was seen at its home location.
access points visited daily (50th, 75th, and 99th percentiles). The daily values are lower than the cumulative
values presented in Figure 14, showing that users visit
different locations on different days. On a daily basis,
the least-mobile users (up to the 50th percentile) visit up
to three access points in a single day. Most users (up to
75% of them) visit up to 3 or 5 access points, and the
most-mobile 25% of users visit between 5 and 25 access points in a single day. As in our study, Tang and
Baker [15] also find differences in user mobility, categorizing many as stationary, some as somewhat mobile,
and a small fraction as highly mobile. Hence, in different environments there exist large personal differences in
user mobility, with most users spending a large fraction
of their time at one location.
5.1 Home location and guest location
5.2 Prevalence
To better model the mobility of a user population or
that of an individual user within the population, we compute two metrics: access-point prevalence in user traces
and user persistence at various locations. These notions
are motivated by Paxson’s analogous definitions [13].
These two metrics characterize mobility patterns independently of the duration of the trace and independently
Transfer Rate (Mb/s)
0.06
LBldg
MBldg
SBldg
0.05
0.04
0.03
0.02
0.01
0
07/20
07/27
08/03
08/10
08/17
Date
(a) Transfer rates at home locations
0.06
Transfer Rate (Mb/s)
Since most users are stationary a large fraction of the
time, we compare user behavior in the location where
they spend most of their time — their home location —
with their behavior in other locations — guest locations.
To determine each user’s home location, we could
simply identify the access point with which they are most
frequently associated. However, visitors or users who use
the Ethernet when working in their offices should not be
assigned a home location. Therefore, we fix a threshold
on the fraction of time that a user must spend with an
access point for it to be considered the user’s home location. We computed home locations for thresholds of
30%, 40% and 50%. With 30% or 40% thresholds, a few
users who divided their time rather equally among various buildings ended-up with a home location in the wrong
building. With a 50% threshold, 10% to 25% of users did
not have a home location, but all home locations were
within buildings where users spent most of their time. We
therefore chose to use a 50% threshold to find user home
locations. Figure 16 shows the cumulative distribution of
the fraction of time spent by users at their home location.
Users spend up to 100% of their time at their home location, with half the users spending at least 60% of their
time there. Given the low daily user mobility found in
the other studies, we expect users in other environments
to show similar distributions of the amount of time spent
at a single home location.
We find that the median user transfers around 20 MB
in a single day at his or her home location which is
similar to the amounts of data transferred by university
users [8, 9]. At guest locations, users transfer approximately half that amount. However, since, by definition,
users spend a large fraction of time at home locations,
time plays a leading role in this difference. Figure 17
shows average daily transfer rates for home and for guest
users (computed as the total amount of data transferred by
a user divided by the total amount of time the user spent
associated with home or guest access points during that
day). Median values are similar for both guest and home
users, but outliers (90th percentile values) show higher
activity at guest locations. Hence, mobility does not seem
to have a negative impact on user transfer rates.
LBldg
MBldg
SBldg
0.05
0.04
0.03
0.02
0.01
0
07/20
07/27
08/03
08/10
08/17
Date
(b) Transfer rates at guest locations
Figure 17:
Average daily data transfer rates per user, at home and at
guest locations. The 10th, 50th, and 90th percentiles are represented.
of the amount of time that users spend on the network.
We start by presenting prevalence metrics. We discuss
persistence in the following section.
Access-point prevalence in a user’s trace is the measure of the fraction of time that a user spends with a given
access point. If a user visits an access point frequently or
spends a lot of time at the access point, the prevalence of
this access point in the user’s trace will be high. Home
locations therefore have high prevalence values whereas
guest locations have lower prevalence values.
The prevalence distribution for a network is a matrix where each row corresponds to an access point and
each column corresponds to a user, as illustrated in Figure 18. We compute one prevalence matrix for each
building to compare mobility within each building. Figure 19 shows the probability distribution of prevalence
values from the LBldg matrix. Zero-value prevalences
have been discarded from the graph as most users visit
only a few access point (so zero-value prevalences domi-
Figure 18:
U2
P rev2,1
P rev2,2
...
P rev2,k
...
...
...
...
...
Un
P revn,1
P revn,2
1
P revn,k
Prevalence matrix for a network of n users and k access
points.
LBldg
0.001/(x^1.75)
LBldg
2.93/(x^1.36)
34/(x^1.87)
0.1
0.01
0.001
0.0001
10
0.8
100
Session duration (min)
0.6
(a) Guest persistence
0.4
0.2
1
0
0
0.2
0.4
0.6
0.8
1
Prevalence
Figure 19:
Probability distribution of prevalence values for LBldg.
Zero valued prevalences are not counted. The other values are in bins
of size 5%. The distribution follows a power law with a low exponent.
nate). The graph shows that users visit a few access points
frequently (prevalences higher than 50% have non-zero
probabilities) while visiting most access points rarely
(prevalences of 0% to 5% are frequent). More precisely,
the prevalence probability distribution follows a power
law with a low exponent as shown in the figure. We obtain almost identical graphs for MBldg (with the same
power law). For SBldg, we find a significantly larger
fraction of prevalences close to 1, pointing towards lower
mobility within that building. Hence, for SBldg, we find
that the distribution follows a power law only for prevalence metrics in the range [0, 0.9]. The difference is most
probably due to the smaller size of the building: there are
only 10 access points in SBldg.
Given these distributions, for each building, we characterize each user with two numbers: the maximum
prevalence and the median prevalence. For a given maximum prevalence, the median is inversely proportional to
the mobility of the user. The more access points a user
visits, the lower the median prevalence.
With these two measures, we categorize users into five
groups as shown in Table 2. By increasing mobility, the
categories are: stationary, occasionally mobile, regular,
somewhat mobile, and highly mobile. Stationary users
stay with a single access point almost all the time so their
maximum prevalence is high and their median is equal to
their maximum. Occasionally mobile users spend most of
their time with a single access point and visit others infre-
Probability of duration
Prevalence frequency
1
Probability of duration
U1
P rev1,1
P rev1,2
...
P rev1,k
AP1
Ap2
...
APk
LBldg
0.92/x
0.1
0.01
0.001
0.0001
10
100
Session duration (min)
(b) Home persistence
Figure 20: Probability distribution of persistence values up to
400 min for home and for guest users in LBldg. Above 400 min, the
probability is close to negligible. Since our sampling interval is 5 min
and often stretches slightly above that value, most persistence values up
to 5 min are rounded up to the 5 min-10 min interval.
quently. Their maximum prevalence is high, while their
median prevalence is low. As the user spends less time
with a single access point, their maximum prevalence decreases. We categorize these users as either somewhat
mobile or highly mobile. Finally, regular users alternate
regularly between a few access points so both their median and maximum prevalences are medium. In all three
buildings, around 40% of users are only occasionally mobile. Few users, however, are totally stationary (around
10% for LBldg and MBldg). Users appear more stationary in SBldg, probably due to the small number of access
points in the building (only 10). A considerable fraction
of users (10% to 40%) is somewhat mobile, but only a
few users are highly mobile.
Maximum Prevalence
(Pmax )
Low
Pmax ∈ [0, 0.33)
Medium
Pmax ∈ [0.33, 0.66)
High
Pmax ∈ [0.66, 1]
Median Prevalence (Pmed )
Low
Med
High
Pmed ∈ [0, 0.25) Pmed ∈ [0.25, 0.50) Pmed ∈ [0.50, 1]
highly mobile
N/A
N/A
(4%,6%,0%)
somewhat mobile
regular
N/A
(38%,29%,11%)
(10%,11%,13%)
occ mobile
N/A
stationary
(39%,44%,44%)
(9%,10%,32%)
Table 2:
5.3 Persistence
Prevalence has one major limitation: It does not take
into account the amount of time users stay associated with
access points. A user who spends a week with an access point and another week with another access point
will have the same prevalence metrics as a user who continuously moves between two access points. To complement the prevalence metric, we compute user persistence
at various locations. The persistence is the amount of
time that a user stays associated with an access point before moving to another access point or leaving the network. Since we poll access points every 5 to 10 min, we
see only visits longer than that interval.
Given our distinction between home and guest locations, we plot the probability distribution of persistence
separately for each group (and for each building). Figure 20 shows the probability distribution of persistence
values up to 400 min separated between home locations
and guest locations. Both distributions follow power
laws. For guest users, the exponents are higher indicating that shorter sessions are more frequent. Additionally, we note a knee in the probability distribution of
guest users, which indicates two different trends in persistence value distributions. The knee appears around
100 min. After that threshold, the power law distribution becomes even steeper indicating that longer sessions
become even rarer above that threshold. The two trends
that appear may be explained as follows. Short sessions
are due to users moving around, attending talks which
last between 20 min and one hour, and also users taking
breaks in common rest areas. Hence, for up to an hour,
various session durations are registered. Longer sessions
are mostly due to meetings that last between an hour and
two hours but hardly ever take longer than that. We find
an almost identical fit for both MBldg and SBldg distributions with a slightly higher exponent after the 100 min
knee for MBldg and slightly different constants.
Balachandran et al. [3] find that user session durations
follow a General Pareto Distribution with a shape parameter of 0.78. This is equivalent to a power law distribution
1
). This distribution is closwith exponent 1.78 (i.e., x1.78
Median home persistence (min)
User categorization based on prevalence metrics. For each building, the fraction of users who belong to each category is indicated under
the category name (LBldg, MBldg, and SBldg respectively).
10000
LBldg
1000
100
10
1
1
10
100
1000
10000
Median guest persistence (min)
Figure 21:
Scatterplot of median guest and median home persistence
values for users who visited LBldg. Users without a home location in
LBldg are assigned a home median of 1 min so they are also represented
on the graph. Home median persistence is almost always greater than
guest median persistence, often by as much as one order of magnitude.
est to what we obtain for guest users, which is what we
might expect since during a conference users are not at
their normal home locations.
Finally, we compare each user’s median persistence at
home and at guest locations. Figure 21 shows the scatterplot obtained for LBldg. A few users have a home median
persistence lower than a guest median persistence. These
are mostly users regularly alternating among a few access points. In most cases, persistence at home is equal
to, or even one or two orders of magnitude greater than,
persistence at guest locations. For users without a home
location, median persistence varies within the same range
as for other users.
In conclusion, the primary characteristic of user mobility is that many users spend a large fraction of their
time in a single location. They visit this location frequently and stay for long periods of time. When they
move away, they do not reduce their data transfer rates,
but they spend short periods of time at any location and
they do not visit the same location frequently.
6
Discussion
In this section, we discuss possible applications of user
mobility and network usage characteristics to wireless
network deployment, to workload generation, and to application design.
6.1 Wireless network design and deployment
Several approaches recently introduced new algorithms to relieve “hot-spots” and dynamically balance
load among access points. Cisco access points [7] balance load between each other (within an overlapping cell)
using the number of users, their error rates, and signal
strength. Balachandran et al. [2] improve load balancing
by explicitly re-directing users to satisfy pre-negotiated
bandwidth range service agreements. Balancing users
across access points is important. As shown in Section 4.2.5, even in a well provisioned network, access
points often experience periods of high demand lasting
a few minutes. In our trace, most of these peaks affected
a single access point at a time. Therefore, quickly load
balancing users could often unload one or two heavilyloaded access points.
Our analysis provides further information that may be
helpful in designing load balancing algorithms: a) we
find high personal differences in network usage; b) users
with high average transfer rates represent a small fraction
of all users; and c) any access point sees only a small
fraction of all users. Additionally, location seems to play
some role in user’s level of activity. Given these network
usage characteristics, we propose that access points keep
mobility and network usage figures for each moderately
active client. They could then better react to overload because they would know how long associated users were
likely to stay and what amount of resources they were
likely to require. Of course, to protect user privacy, access points should neither make this data openly available
nor communicate it to other access points.
For each associated user, access points already keep
counters of bytes and packets transferred. For users transferring more than 2Mbps or 3Mbps, each access point
could preserve a running average of data transferred as
well as the user’s peak transfer rates. Each access point
could also preserve a running average of persistence values, updating them as follows every time a user deassociates from an access point: Puser,t = α∗Puser,t−1 +
(1 − α)Duser , where Puser,t−1 is the previous average
persistence for a user, Duser is the amount of time the
user remained associated this time, and α ∈ [0, 1] is a
factor adjusting the importance of history over the latest
value. The average transfer rate, Xuser,t , could be computed in a similar manner. In a situation of overload, an
access point could choose to re-direct a user to another lo-
cation if this user had a history of using a lot of resources
(high X), but not staying very long (low P ). The access
point would not waste resources redirecting users that are
always idle. It would also avoid re-directing users who
stay with the access point for prolonged periods of time
(perhaps those who have their office there).
Access point popularity is another useful metric for
network deployment. To provide coverage, we find that
some access points are deployed in locations where they
are seldom used. These access points could self-tune to
reduce their power consumption or increase their coverage (while decreasing maximum data rates). Additionally, access point could compute and compare user persistence and prevalence metrics to determine their relative popularity. Such relative popularity metrics, coupled with load metrics, would help system administrators
determine the most appropriate locations for new access
points. Ideally, system administrators want to deploy extra access points before users see performance degradation, so they need other metrics to determine where new
access points should be added.
6.2 Refining workload generation
Several tools exist to simulate wireless networks [11,
12, 14]. They model characteristics of the wireless network quite accurately, but they require users to dynamically define the location of a node or to define trajectories. However, tools could use the concept of “home
locations” as well as power law distributions for persistence and prevalence to simulate user mobility automatically. Persistence could serve to determine how long a
user stays at a location on average, whereas prevalence
could serve to determine which location the user visits.
Additionally, we find it reasonable to use scaled down
population models to determine the number of users
present on the simulated network at any point in time.
6.3 Guiding application design
Knowledge of network usage characteristics may also
prove helpful in designing applications for mobile environments. For example, since users spend a large fraction
of time at their home location the design of mobile systems might benefit from optimizations for this particular
usage pattern. An application may, for example, keep information about each user at their home location.
Additionally, in all environments studied, most users
visit only a few locations during a day, and spend a large
fraction of their time at their home location before visiting other locations on subsequent days. This specific mobility pattern should also influence design decisions. For
example, when synchronizing application data, it would
be an appropriate design choice to designate the home
location as the “master” replica.
7
Conclusion
In this paper, we presented the analysis of results from
a four week trace gathered in a large corporate environment and showing the network usage of 1366 different
users. Analyzing mobility and network usage, we find
several characteristics, many of which are shared by users
in other environments such as university campuses and
public networks.
In spite of increasing popularity of wireless networks,
the number of days each user appears on the network is
highly variable among different users. However, the general patterns in the numbers of users per day and the number of users per hour follow a regular office schedule.
Load is unevenly distributed across access points.
Some, located in popular areas such as large auditoriums,
often see high numbers of users simultaneously associated with them (up to 30). Others, located in less visited
areas, are usually idle. We find that the amount of traffic at an access point is weakly dependent on the number
of users present or the time of day. Most traffic is due
to a small fraction of active users: the most-active 10%
of users are responsible for more than 40% of the data
transferred at 30% of locations. Load is also somewhat
related to access point location, as we find that location
impacts user transfer rate significantly for 30% of active
users. Additionally, user’s average level of activity follows a power law distribution.
We introduce persistence and prevalence to characterize and classify user mobility. Probability distributions
of both metrics follow power law distributions. Persistence at guest locations also has a higher exponent than
persistence at home locations, clearly showing that users
associate with access points longer when staying at their
home locations. Using prevalence, users can be categorized into mostly stationary, occasionally mobile, regular,
somewhat mobile, and highly mobile. We find that 50%
to 80% of users fall into the occasionally and somewhat
mobile categories. Finally, we find that mobility does not
influence user level of activity on the network. However,
most devices in our study were laptops; mobility results
may become different as PDAs and other small devices
become more popular.
We plan to repeat this study, using SNMP in combination with syslog and tcpdump as well as monitoring software on mobile devices. Our goal is to get more detailed
information on network usage and develop more detailed
models of both mobility and network usage.
for helping us gain access to the access points. We gratefully acknowledge the help of David Kotz with SNMP
and data collection scripts. We thank Hari Balakrishnan
and Chuck Blake for their suggestions on result presentation and analysis. We also thank everyone who read and
commented on draft versions of this paper.
References
[1] B. Alexander and S. Snow. Preparing for wireless LANs: Secrets to successful wireless deployment. Packet Magazine. Cisco
Systems. http://www.cisco.com/warp/public/784/
packet/apr02/p36-cover.html, April 2002.
[2] Anand Balachandran, Paramvir Bahl, and Geoffrey M. Voelker.
Hot-spot congestion relief and user service guarantees in publicarea wireless networks. In Proc. of the 4th IEEE Workshop on Mobile Computing Systems and Applications (WMCSA 2002). IEEE
Computer Society, June 2002.
[3] Anand Balachandran, Geoffrey M. Voelker, Paramvir Bahl, and
P. Venkat Rangan. Characterizing user behavior and network performance in a public wireless LAN. In Proc. of ACM SIGMETRICS’02. ACM Press, June 2002.
[4] J. Case, M. Fedor, M. Schoffstall, and J. Davin. RFC1157: A simple network management protocol (SNMP). http://ietf.
org/rfc/rfc1157.txt?number=1157, 1990.
[5] Alex Hills. Wireless Andrew. IEEE Spectrum, 36(6):49–53, June
1999.
[6] William W. Hines and Douglas C. Montgomery. Probability and
Statistics in Engineering and Management Science. Third Edition.
John Wiley and Sons, 1990.
[7] ”Cisco Systems Inc.”. Data sheet for Cisco Aironet 350 Series
access points. http://www.cisco.com/warp/public/
cc/pd/witc/ao350ap/prodlit/, July 2001.
[8] David Kotz and Kobby Essien. Analysis of a campus-wide wireless network. In Proc. of the Eigth Annual Int. Conf. on Mobile
Computing and Networking (MobiCom). ACM Press, September
2002.
[9] David Kotz and Kobby Essien. Characterizing usage of a campuswide wireless network. Technical Report TR2002-423, Dept. of
Computer Science, Dartmouth College, March 2002.
[10] Kevin Lai, Mema Roussopoulos, Diane Tang, Xinhua Zhao, and
Mary Baker. Experiences with a mobile testbed. In Proc. of the
Second International Conference on Worldwide Computing and
its Applications (WWCA’98), March 1998.
[11] ns-2. The Network Simulator.
nsnam/ns/.
http://www.isi.edu/
[12] Inc. OPNET Technologies. OPNET Modeler. http://www.
opnet.com/products/modeler/home.html, 2002.
[13] Vern Paxson. End-to-end routing behavior in the internet. In Proc.
of the ACM SIGCOMM Conference. ACM Press, August 1996.
[14] Rice Monarch Project. http://www.monarch.cs.rice.
edu/cmu-ns.html, 1999.
[15] Diane Tang and Mary Baker. Analysis of a local-area wireless
network. In Proc. of the Sixth Annual Int. Conf. on Mobile Computing and Networking (MobiCom). ACM Press, August 2000.
Acknowledgmenents
[16] Diane Tang and Mary Baker. Analysis of a metropolitan-area
wireless network. Wireless Networks, 8(2/3):107–120, 2002.
We thank Apratim Purakayastha for his helpful suggestions throughout the project. We thank Drew Wyskida
[17] ZDNet News. 802.11 group looking for more ’hot spots’.
http://zdnet.com.com/2100-1105-936659.html,
June 2002.